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A new study has found that an artificial intelligence (AI) tool can accurately predict which patients who have been newly infected with the 2019 coronavirus disease (COVID-19) can go on to develop severe respiratory disease. The study was led by the NYU Grossman School of Medicine (New York City, NY) and NYU Courant Institute of Mathematical Sciences (New York City, NY) in partnership with Wenzhou Central Hospital (Wenzhou, China) and Cangnan People’s Hospital (Wenzhou, China). The study also revealed the best indicators of future severity, and found that they were not as expected.

For the study, the researchers gathered demographic, laboratory, and radiological findings collected from 53 patients who tested positive in January 2020 for the SARS-CoV-2 virus at the two Chinese hospitals. The patients initially had typically mild symptoms, including cough, fever, and stomach upset, although severe symptoms, including pneumonia, developed in a minority of patients within a week. The study aimed to determine whether AI techniques could help to accurately predict which patients with the virus would go on to develop acute respiratory distress syndrome (ARDS). The researchers also designed computer models that make decisions based on the data fed into them, with the programs becoming “smarter” with more data that they considered. The study used decision trees that track series of decisions between options and that model the potential consequences of choices at each step in a pathway.

The researchers were surprised to find that certain patterns seen in lung images, fever, and strong immune responses, were not useful in predicting which patients with initial, mild symptoms would go on to develop severe lung disease. Instead, the researchers found that the new AI tool identified changes in three features—levels of the liver enzyme alanine aminotransferase (ALT), reported myalgia, and hemoglobin levels—which were most accurately predictive of subsequent, severe disease. This, combined with other factors, allowed the AI tool to predict the risk of ARDS in patients infected with COVID-19 with up to 80% accuracy.

“While work remains to further validate our model, it holds promise as another tool to predict the patients most vulnerable to the virus, but only in support of physicians’ hard-won clinical experience in treating viral infections,” said corresponding study author Megan Coffee, MD, PhD, clinical assistant professor in the Department of Medicine and member of the Division of Infectious Diseases and Immunology at NYU Langone.

“Our goal was to design and deploy a decision-support tool using AI capabilities—mostly predictive analytics—to flag future clinical coronavirus severity,” said co-author Anasse Bari, PhD, a clinical assistant professor in computer science at NYU Courant Institute of Mathematical Science. “We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds, and who can safely go home, when hospital resources are stretched thin.”